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Bayesian comparison of production function-based and time-series GDP models

Author

Listed:
  • Jacek Osiewalski

    (Cracow University of Economics)

  • Justyna Wróblewska

    (Cracow University of Economics)

  • Kamil Makieła

    (Cracow University of Economics)

Abstract

A purely Bayesian vector autoregression (VAR) framework is proposed to formulate and compare tri-variate models for the logs of the economy-wide aggregates of output and inputs (physical capital and labour). The framework is derived based on the theory of the aggregate production function, but at the same time, accounts for the dynamic properties of macroeconomic data, which makes it particularly appealing for modelling GDP. Next, using the proposed framework we confront a-theoretical time-series models with those that are based on aggregate production function-type relations. The common knowledge about capital and labour elasticities of output as well as on their sum is used in order to formulate prior distribution for each tri-variate model, favouring the linearly homogenous Cobb–Douglas production function-type relation. In spite of this, production function-based co-integration models fail empirical comparisons with simple VAR structures, which describe the three aggregates by three stochastic trends.

Suggested Citation

  • Jacek Osiewalski & Justyna Wróblewska & Kamil Makieła, 2020. "Bayesian comparison of production function-based and time-series GDP models," Empirical Economics, Springer, vol. 58(3), pages 1355-1380, March.
    • Handle: RePEc:spr:empeco:v:58:y:2020:i:3:d:10.1007_s00181-018-1575-8
    DOI: 10.1007/s00181-018-1575-8
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Thomas H. W. Ziesemer, 2023. "Semi-endogenous growth in a non-Walrasian DSEM for Brazil: estimation and simulation of changes in foreign income, human capital, R&D, and terms of trade," Economic Change and Restructuring, Springer, vol. 56(2), pages 1147-1183, April.
    2. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    3. Thomas H. W. Ziesemer, 2021. "Semi-endogenous growth models with domestic and foreign private and public R&D linked to VECMs," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 30(6), pages 621-642, August.
    4. Jakub Boratyński & Jacek Osiewalski, 2021. "Bayesian Estimation of Capital Stock and Depreciation in the Production Function Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(4), pages 455-486, December.
    5. Kamil Makieła & Błażej Mazur & Jakub Głowacki, 2022. "The Impact of Renewable Energy Supply on Economic Growth and Productivity," Energies, MDPI, vol. 15(13), pages 1-13, June.

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    More about this item

    Keywords

    Bayesian inference; VAR models; Economic growth models; Co-integration analysis; Aggregate production function; Potential output;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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